An Unknown Risk Analysis and Evaluation Method for Distribution Edge Computing Networks

Distribution edge computing networks require robust risk analysis and evaluation, especially for unknown attacks. This work addresses unknown risk analysis using the Bayesian hazard graph, proposing an algorithm tailored to the context of edge computing networks. The algorithm calculates unknown risks using a noise model and incorporates the CVSS scoring standard to create a conditional probability table. It continuously updates probability distributions with known information and real-time feedback, making it well-suited for distribution edge computing networks.